Overview

Dataset statistics

Number of variables36
Number of observations6349
Missing cells67490
Missing cells (%)29.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 MiB
Average record size in memory296.0 B

Variable types

Categorical6
Numeric28
Unsupported2

Alerts

date has a high cardinality: 683 distinct values High cardinality
race_no is highly correlated with prize and 2 other fieldsHigh correlation
distance is highly correlated with sec_time2 and 5 other fieldsHigh correlation
prize is highly correlated with race_no and 1 other fieldsHigh correlation
race_class is highly correlated with race_no and 2 other fieldsHigh correlation
sec_time1 is highly correlated with sec_time2 and 6 other fieldsHigh correlation
sec_time2 is highly correlated with distance and 8 other fieldsHigh correlation
sec_time3 is highly correlated with distance and 5 other fieldsHigh correlation
sec_time4 is highly correlated with distanceHigh correlation
sec_time5 is highly correlated with place_dividend4High correlation
sec_time6 is highly correlated with time3High correlation
time1 is highly correlated with sec_time1 and 6 other fieldsHigh correlation
time2 is highly correlated with sec_time1 and 6 other fieldsHigh correlation
time3 is highly correlated with sec_time1 and 10 other fieldsHigh correlation
time4 is highly correlated with distance and 11 other fieldsHigh correlation
time5 is highly correlated with distance and 7 other fieldsHigh correlation
time6 is highly correlated with distance and 9 other fieldsHigh correlation
place_combination1 is highly correlated with win_combination1High correlation
place_combination2 is highly correlated with win_combination2High correlation
place_combination3 is highly correlated with place_combination4High correlation
place_combination4 is highly correlated with race_no and 3 other fieldsHigh correlation
place_dividend1 is highly correlated with win_dividend1High correlation
place_dividend2 is highly correlated with win_dividend2High correlation
place_dividend4 is highly correlated with sec_time5 and 1 other fieldsHigh correlation
win_combination1 is highly correlated with place_combination1High correlation
win_dividend1 is highly correlated with place_dividend1High correlation
win_combination2 is highly correlated with sec_time1 and 4 other fieldsHigh correlation
win_dividend2 is highly correlated with time4 and 1 other fieldsHigh correlation
race_no is highly correlated with place_combination4High correlation
distance is highly correlated with sec_time2 and 4 other fieldsHigh correlation
prize is highly correlated with time6High correlation
race_class is highly correlated with time6High correlation
sec_time1 is highly correlated with sec_time2 and 7 other fieldsHigh correlation
sec_time2 is highly correlated with distance and 9 other fieldsHigh correlation
sec_time3 is highly correlated with distance and 2 other fieldsHigh correlation
sec_time4 is highly correlated with distanceHigh correlation
sec_time5 is highly correlated with place_combination4 and 1 other fieldsHigh correlation
time1 is highly correlated with sec_time1 and 7 other fieldsHigh correlation
time2 is highly correlated with sec_time1 and 6 other fieldsHigh correlation
time3 is highly correlated with sec_time1 and 6 other fieldsHigh correlation
time4 is highly correlated with sec_time1 and 8 other fieldsHigh correlation
time5 is highly correlated with distance and 7 other fieldsHigh correlation
time6 is highly correlated with distance and 9 other fieldsHigh correlation
place_combination1 is highly correlated with win_combination1High correlation
place_combination2 is highly correlated with win_combination2High correlation
place_combination4 is highly correlated with race_no and 2 other fieldsHigh correlation
place_dividend1 is highly correlated with win_dividend1High correlation
place_dividend2 is highly correlated with win_dividend2High correlation
place_dividend4 is highly correlated with sec_time5High correlation
win_combination1 is highly correlated with place_combination1High correlation
win_dividend1 is highly correlated with place_dividend1High correlation
win_combination2 is highly correlated with time4 and 1 other fieldsHigh correlation
win_dividend2 is highly correlated with sec_time1 and 2 other fieldsHigh correlation
race_no is highly correlated with prize and 1 other fieldsHigh correlation
distance is highly correlated with sec_time3 and 2 other fieldsHigh correlation
prize is highly correlated with race_no and 1 other fieldsHigh correlation
race_class is highly correlated with race_no and 2 other fieldsHigh correlation
sec_time1 is highly correlated with sec_time2 and 5 other fieldsHigh correlation
sec_time2 is highly correlated with sec_time1 and 5 other fieldsHigh correlation
sec_time3 is highly correlated with distance and 1 other fieldsHigh correlation
sec_time5 is highly correlated with place_dividend4High correlation
time1 is highly correlated with sec_time1 and 5 other fieldsHigh correlation
time2 is highly correlated with sec_time1 and 6 other fieldsHigh correlation
time3 is highly correlated with sec_time1 and 6 other fieldsHigh correlation
time4 is highly correlated with sec_time1 and 8 other fieldsHigh correlation
time5 is highly correlated with distance and 5 other fieldsHigh correlation
time6 is highly correlated with distance and 7 other fieldsHigh correlation
place_combination1 is highly correlated with win_combination1High correlation
place_combination2 is highly correlated with win_combination2High correlation
place_dividend1 is highly correlated with win_dividend1High correlation
place_dividend2 is highly correlated with win_dividend2High correlation
place_dividend4 is highly correlated with sec_time5High correlation
win_combination1 is highly correlated with place_combination1High correlation
win_dividend1 is highly correlated with place_dividend1High correlation
win_combination2 is highly correlated with time4 and 1 other fieldsHigh correlation
win_dividend2 is highly correlated with place_dividend2High correlation
surface is highly correlated with goingHigh correlation
going is highly correlated with surfaceHigh correlation
venue is highly correlated with config and 7 other fieldsHigh correlation
race_no is highly correlated with horse_ratings and 2 other fieldsHigh correlation
config is highly correlated with venue and 6 other fieldsHigh correlation
surface is highly correlated with config and 2 other fieldsHigh correlation
distance is highly correlated with horse_ratings and 13 other fieldsHigh correlation
going is highly correlated with surface and 6 other fieldsHigh correlation
horse_ratings is highly correlated with race_no and 8 other fieldsHigh correlation
prize is highly correlated with horse_ratings and 4 other fieldsHigh correlation
race_class is highly correlated with race_no and 5 other fieldsHigh correlation
sec_time1 is highly correlated with distance and 9 other fieldsHigh correlation
sec_time2 is highly correlated with distance and 11 other fieldsHigh correlation
sec_time3 is highly correlated with distance and 9 other fieldsHigh correlation
sec_time4 is highly correlated with distance and 7 other fieldsHigh correlation
sec_time5 is highly correlated with distance and 4 other fieldsHigh correlation
sec_time6 is highly correlated with venue and 8 other fieldsHigh correlation
time1 is highly correlated with distance and 9 other fieldsHigh correlation
time2 is highly correlated with distance and 10 other fieldsHigh correlation
time3 is highly correlated with venue and 13 other fieldsHigh correlation
time4 is highly correlated with venue and 17 other fieldsHigh correlation
time5 is highly correlated with venue and 16 other fieldsHigh correlation
time6 is highly correlated with venue and 16 other fieldsHigh correlation
place_combination1 is highly correlated with win_combination1High correlation
place_combination2 is highly correlated with sec_time6 and 2 other fieldsHigh correlation
place_combination3 is highly correlated with place_dividend4 and 1 other fieldsHigh correlation
place_combination4 is highly correlated with config and 11 other fieldsHigh correlation
place_dividend1 is highly correlated with win_dividend1High correlation
place_dividend2 is highly correlated with place_dividend4 and 1 other fieldsHigh correlation
place_dividend3 is highly correlated with place_dividend4 and 1 other fieldsHigh correlation
place_dividend4 is highly correlated with going and 5 other fieldsHigh correlation
win_combination1 is highly correlated with place_combination1High correlation
win_dividend1 is highly correlated with place_dividend1High correlation
win_combination2 is highly correlated with venue and 9 other fieldsHigh correlation
win_dividend2 is highly correlated with venue and 15 other fieldsHigh correlation
prize has 462 (7.3%) missing values Missing
sec_time4 has 2715 (42.8%) missing values Missing
sec_time5 has 5528 (87.1%) missing values Missing
sec_time6 has 6234 (98.2%) missing values Missing
sec_time7 has 6349 (100.0%) missing values Missing
time4 has 2715 (42.8%) missing values Missing
time5 has 5528 (87.1%) missing values Missing
time6 has 6234 (98.2%) missing values Missing
time7 has 6349 (100.0%) missing values Missing
place_combination4 has 6326 (99.6%) missing values Missing
place_dividend4 has 6326 (99.6%) missing values Missing
win_combination2 has 6337 (99.8%) missing values Missing
win_dividend2 has 6337 (99.8%) missing values Missing
date is uniformly distributed Uniform
sec_time7 is an unsupported type, check if it needs cleaning or further analysis Unsupported
time7 is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2022-01-09 21:23:12.368242
Analysis finished2022-01-09 21:24:37.544243
Duration1 minute and 25.18 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

date
Categorical

HIGH CARDINALITY
UNIFORM

Distinct683
Distinct (%)10.8%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
2000-09-17
 
11
1998-02-01
 
11
2001-10-30
 
11
2003-09-14
 
11
2003-09-18
 
11
Other values (678)
6294 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1997-06-02
2nd row1997-06-02
3rd row1997-06-02
4th row1997-06-02
5th row1997-06-02

Common Values

ValueCountFrequency (%)
2000-09-1711
 
0.2%
1998-02-0111
 
0.2%
2001-10-3011
 
0.2%
2003-09-1411
 
0.2%
2003-09-1811
 
0.2%
1998-09-1811
 
0.2%
2001-09-1811
 
0.2%
1999-12-1911
 
0.2%
2003-11-0811
 
0.2%
2001-09-0211
 
0.2%
Other values (673)6239
98.3%

Length

2022-01-09T21:24:37.742286image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2000-09-1711
 
0.2%
1997-07-0611
 
0.2%
2000-12-2511
 
0.2%
1997-10-1511
 
0.2%
1997-06-2911
 
0.2%
1997-08-0311
 
0.2%
2004-12-2611
 
0.2%
2005-06-1811
 
0.2%
2005-07-0311
 
0.2%
1997-10-2611
 
0.2%
Other values (673)6239
98.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

venue
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
ST
4006 
HV
2343 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowST
2nd rowST
3rd rowST
4th rowST
5th rowST

Common Values

ValueCountFrequency (%)
ST4006
63.1%
HV2343
36.9%

Length

2022-01-09T21:24:37.873329image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-09T21:24:37.931342image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
st4006
63.1%
hv2343
36.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

race_no
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.226807371
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.2 KiB
2022-01-09T21:24:37.985354image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q37
95-th percentile10
Maximum11
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.795018675
Coefficient of variation (CV)0.5347468304
Kurtosis-1.043399629
Mean5.226807371
Median Absolute Deviation (MAD)2
Skewness0.140026775
Sum33185
Variance7.812129393
MonotonicityNot monotonic
2022-01-09T21:24:38.067373image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1683
10.8%
2682
10.7%
3682
10.7%
5682
10.7%
6682
10.7%
7682
10.7%
8682
10.7%
4681
10.7%
9402
6.3%
10383
6.0%
ValueCountFrequency (%)
1683
10.8%
2682
10.7%
3682
10.7%
4681
10.7%
5682
10.7%
6682
10.7%
7682
10.7%
8682
10.7%
9402
6.3%
10383
6.0%
ValueCountFrequency (%)
11108
 
1.7%
10383
6.0%
9402
6.3%
8682
10.7%
7682
10.7%
6682
10.7%
5682
10.7%
4681
10.7%
3682
10.7%
2682
10.7%

config
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
A
2209 
C
1174 
C+3
984 
B
819 
B+2
600 

Length

Max length3
Median length1
Mean length1.676326981
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A2209
34.8%
C1174
18.5%
C+3984
15.5%
B819
 
12.9%
B+2600
 
9.5%
A+3563
 
8.9%

Length

2022-01-09T21:24:38.161395image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-09T21:24:38.224411image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
a2209
34.8%
c1174
18.5%
c+3984
15.5%
b819
 
12.9%
b+2600
 
9.5%
a+3563
 
8.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

surface
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
0
5656 
1
693 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
05656
89.1%
1693
 
10.9%

Length

2022-01-09T21:24:38.295420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-09T21:24:38.349442image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
05656
89.1%
1693
 
10.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

distance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1419.113246
Minimum1000
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.2 KiB
2022-01-09T21:24:38.396432image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1000
Q11200
median1400
Q31650
95-th percentile1800
Maximum2400
Range1400
Interquartile range (IQR)450

Descriptive statistics

Standard deviation281.4687454
Coefficient of variation (CV)0.1983412854
Kurtosis-0.1800770846
Mean1419.113246
Median Absolute Deviation (MAD)200
Skewness0.4703788859
Sum9009950
Variance79224.65463
MonotonicityNot monotonic
2022-01-09T21:24:38.471450image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
12002017
31.8%
16501095
17.2%
14001081
17.0%
1000698
 
11.0%
1600637
 
10.0%
1800589
 
9.3%
2000117
 
1.8%
220090
 
1.4%
240025
 
0.4%
ValueCountFrequency (%)
1000698
 
11.0%
12002017
31.8%
14001081
17.0%
1600637
 
10.0%
16501095
17.2%
1800589
 
9.3%
2000117
 
1.8%
220090
 
1.4%
240025
 
0.4%
ValueCountFrequency (%)
240025
 
0.4%
220090
 
1.4%
2000117
 
1.8%
1800589
 
9.3%
16501095
17.2%
1600637
 
10.0%
14001081
17.0%
12002017
31.8%
1000698
 
11.0%

going
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
GOOD
4123 
GOOD TO FIRM
1613 
GOOD TO YIELDING
 
333
YIELDING
 
86
FAST
 
70
Other values (5)
 
124

Length

Max length16
Median length4
Mean length6.793510789
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGOOD TO FIRM
2nd rowGOOD TO FIRM
3rd rowGOOD TO FIRM
4th rowGOOD TO FIRM
5th rowGOOD TO FIRM

Common Values

ValueCountFrequency (%)
GOOD4123
64.9%
GOOD TO FIRM1613
 
25.4%
GOOD TO YIELDING333
 
5.2%
YIELDING86
 
1.4%
FAST70
 
1.1%
WET SLOW61
 
1.0%
WET FAST20
 
0.3%
SLOW15
 
0.2%
YIELDING TO SOFT14
 
0.2%
SOFT14
 
0.2%

Length

2022-01-09T21:24:38.566471image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-01-09T21:24:38.634485image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
good6069
58.6%
to1960
 
18.9%
firm1613
 
15.6%
yielding433
 
4.2%
fast90
 
0.9%
wet81
 
0.8%
slow76
 
0.7%
soft28
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

horse_ratings
Categorical

HIGH CORRELATION

Distinct31
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size99.2 KiB
60-40
2196 
80-60
1786 
40-15
409 
100-80
407 
G
312 
Other values (26)
1239 

Length

Max length7
Median length5
Mean length4.850685147
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row40-15
2nd row40-15
3rd row60-40
4th row120-95
5th row60-40

Common Values

ValueCountFrequency (%)
60-402196
34.6%
80-601786
28.1%
40-15409
 
6.4%
100-80407
 
6.4%
G312
 
4.9%
40-0271
 
4.3%
85-60193
 
3.0%
40-10189
 
3.0%
105-80154
 
2.4%
65-4062
 
1.0%
Other values (21)370
 
5.8%

Length

2022-01-09T21:24:38.739516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
60-402196
34.6%
80-601786
28.1%
40-15409
 
6.4%
100-80407
 
6.4%
g312
 
4.9%
40-0271
 
4.3%
85-60193
 
3.0%
40-10189
 
3.0%
105-80154
 
2.4%
65-4062
 
1.0%
Other values (21)370
 
5.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

prize
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct71
Distinct (%)1.2%
Missing462
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean1134790.386
Minimum485000
Maximum25000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.2 KiB
2022-01-09T21:24:39.229619image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum485000
5-th percentile525000
Q1675000
median840000
Q31060000
95-th percentile2200000
Maximum25000000
Range24515000
Interquartile range (IQR)385000

Descriptive statistics

Standard deviation1749155.63
Coefficient of variation (CV)1.54139095
Kurtosis82.93122949
Mean1134790.386
Median Absolute Deviation (MAD)200000
Skewness8.499581927
Sum6680511000
Variance3.059545418 × 1012
MonotonicityNot monotonic
2022-01-09T21:24:39.340153image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
850000562
 
8.9%
625000364
 
5.7%
800000297
 
4.7%
760000282
 
4.4%
725000280
 
4.4%
640000278
 
4.4%
700000274
 
4.3%
675000273
 
4.3%
1165000247
 
3.9%
1060000246
 
3.9%
Other values (61)2784
43.8%
(Missing)462
 
7.3%
ValueCountFrequency (%)
485000126
 
2.0%
500000100
 
1.6%
525000108
 
1.7%
540000114
 
1.8%
550000116
 
1.8%
575000114
 
1.8%
600000110
 
1.7%
625000364
5.7%
63000041
 
0.6%
640000278
4.4%
ValueCountFrequency (%)
250000003
 
< 0.1%
230000003
 
< 0.1%
220000002
 
< 0.1%
200000008
0.1%
185000003
 
< 0.1%
180000002
 
< 0.1%
165000003
 
< 0.1%
160000008
0.1%
150000004
 
0.1%
1400000012
0.2%

race_class
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.893684045
Minimum0
Maximum13
Zeros12
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size99.2 KiB
2022-01-09T21:24:39.600369image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median4
Q34
95-th percentile6
Maximum13
Range13
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.992868379
Coefficient of variation (CV)0.5118207733
Kurtosis9.697777251
Mean3.893684045
Median Absolute Deviation (MAD)1
Skewness2.772853585
Sum24721
Variance3.971524375
MonotonicityNot monotonic
2022-01-09T21:24:39.675995image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
42319
36.5%
32000
31.5%
5892
 
14.0%
2568
 
8.9%
1229
 
3.6%
11122
 
1.9%
13100
 
1.6%
1256
 
0.9%
651
 
0.8%
012
 
0.2%
ValueCountFrequency (%)
012
 
0.2%
1229
 
3.6%
2568
 
8.9%
32000
31.5%
42319
36.5%
5892
 
14.0%
651
 
0.8%
11122
 
1.9%
1256
 
0.9%
13100
 
1.6%
ValueCountFrequency (%)
13100
 
1.6%
1256
 
0.9%
11122
 
1.9%
651
 
0.8%
5892
 
14.0%
42319
36.5%
32000
31.5%
2568
 
8.9%
1229
 
3.6%
012
 
0.2%

sec_time1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct660
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.69946606
Minimum12.39
Maximum30.03
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.2 KiB
2022-01-09T21:24:39.769018image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum12.39
5-th percentile12.99
Q113.69
median23.72
Q324.7
95-th percentile28.16
Maximum30.03
Range17.64
Interquartile range (IQR)11.01

Descriptive statistics

Standard deviation5.880318926
Coefficient of variation (CV)0.2840807058
Kurtosis-1.649093239
Mean20.69946606
Median Absolute Deviation (MAD)4.05
Skewness-0.2802923861
Sum131420.91
Variance34.57815068
MonotonicityNot monotonic
2022-01-09T21:24:39.881043image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.5958
 
0.9%
13.6258
 
0.9%
13.5656
 
0.9%
13.6754
 
0.9%
13.6453
 
0.8%
13.752
 
0.8%
13.5351
 
0.8%
13.5248
 
0.8%
13.5545
 
0.7%
13.6944
 
0.7%
Other values (650)5830
91.8%
ValueCountFrequency (%)
12.392
< 0.1%
12.412
< 0.1%
12.472
< 0.1%
12.482
< 0.1%
12.491
 
< 0.1%
12.511
 
< 0.1%
12.522
< 0.1%
12.534
0.1%
12.541
 
< 0.1%
12.554
0.1%
ValueCountFrequency (%)
30.031
< 0.1%
29.641
< 0.1%
29.51
< 0.1%
29.471
< 0.1%
29.341
< 0.1%
29.251
< 0.1%
29.241
< 0.1%
29.231
< 0.1%
29.221
< 0.1%
29.192
< 0.1%

sec_time2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct501
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.82674909
Minimum20.06
Maximum27.41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.2 KiB
2022-01-09T21:24:39.995054image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum20.06
5-th percentile21.11
Q122.14
median22.8
Q323.45
95-th percentile24.62
Maximum27.41
Range7.35
Interquartile range (IQR)1.31

Descriptive statistics

Standard deviation1.044997784
Coefficient of variation (CV)0.04577952733
Kurtosis0.09073278602
Mean22.82674909
Median Absolute Deviation (MAD)0.66
Skewness0.2290234207
Sum144927.03
Variance1.092020369
MonotonicityNot monotonic
2022-01-09T21:24:40.107094image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.6755
 
0.9%
22.9151
 
0.8%
22.7750
 
0.8%
22.6247
 
0.7%
22.8647
 
0.7%
22.2845
 
0.7%
23.1645
 
0.7%
23.0245
 
0.7%
23.1244
 
0.7%
22.9243
 
0.7%
Other values (491)5877
92.6%
ValueCountFrequency (%)
20.061
 
< 0.1%
20.191
 
< 0.1%
20.21
 
< 0.1%
20.231
 
< 0.1%
20.272
< 0.1%
20.32
< 0.1%
20.311
 
< 0.1%
20.331
 
< 0.1%
20.341
 
< 0.1%
20.364
0.1%
ValueCountFrequency (%)
27.411
< 0.1%
26.811
< 0.1%
26.671
< 0.1%
26.421
< 0.1%
26.412
< 0.1%
26.361
< 0.1%
26.311
< 0.1%
26.21
< 0.1%
26.191
< 0.1%
26.171
< 0.1%

sec_time3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct451
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.83074342
Minimum21.2
Maximum27.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.2 KiB
2022-01-09T21:24:40.218103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum21.2
5-th percentile22.55
Q123.21
median23.72
Q324.38
95-th percentile25.38
Maximum27.58
Range6.38
Interquartile range (IQR)1.17

Descriptive statistics

Standard deviation0.870355392
Coefficient of variation (CV)0.03652237685
Kurtosis0.08300639305
Mean23.83074342
Median Absolute Deviation (MAD)0.58
Skewness0.4553002359
Sum151301.39
Variance0.7575185084
MonotonicityNot monotonic
2022-01-09T21:24:40.319127image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.5552
 
0.8%
23.5650
 
0.8%
23.5949
 
0.8%
23.8849
 
0.8%
23.7846
 
0.7%
24.3844
 
0.7%
23.9244
 
0.7%
23.5244
 
0.7%
23.3143
 
0.7%
23.7243
 
0.7%
Other values (441)5885
92.7%
ValueCountFrequency (%)
21.21
< 0.1%
21.431
< 0.1%
21.551
< 0.1%
21.581
< 0.1%
21.611
< 0.1%
21.641
< 0.1%
21.652
< 0.1%
21.71
< 0.1%
21.711
< 0.1%
21.722
< 0.1%
ValueCountFrequency (%)
27.581
< 0.1%
27.441
< 0.1%
27.411
< 0.1%
27.271
< 0.1%
27.171
< 0.1%
27.141
< 0.1%
26.941
< 0.1%
26.911
< 0.1%
26.881
< 0.1%
26.841
< 0.1%

sec_time4
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct419
Distinct (%)11.5%
Missing2715
Missing (%)42.8%
Infinite0
Infinite (%)0.0%
Mean23.8528536
Minimum21.4
Maximum28.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.2 KiB
2022-01-09T21:24:40.422293image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum21.4
5-th percentile22.66
Q123.3
median23.77
Q324.33
95-th percentile25.2935
Maximum28.92
Range7.52
Interquartile range (IQR)1.03

Descriptive statistics

Standard deviation0.8202770386
Coefficient of variation (CV)0.03438905266
Kurtosis1.359217292
Mean23.8528536
Median Absolute Deviation (MAD)0.51
Skewness0.7012126331
Sum86681.27
Variance0.6728544201
MonotonicityNot monotonic
2022-01-09T21:24:40.527721image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.5828
 
0.4%
23.6727
 
0.4%
23.6127
 
0.4%
23.3727
 
0.4%
23.3426
 
0.4%
23.4626
 
0.4%
23.3926
 
0.4%
23.8425
 
0.4%
23.6925
 
0.4%
23.4424
 
0.4%
Other values (409)3373
53.1%
(Missing)2715
42.8%
ValueCountFrequency (%)
21.41
< 0.1%
21.551
< 0.1%
21.671
< 0.1%
21.681
< 0.1%
21.771
< 0.1%
21.781
< 0.1%
21.81
< 0.1%
21.811
< 0.1%
21.911
< 0.1%
21.931
< 0.1%
ValueCountFrequency (%)
28.921
< 0.1%
27.741
< 0.1%
27.611
< 0.1%
27.561
< 0.1%
27.51
< 0.1%
27.251
< 0.1%
27.091
< 0.1%
27.081
< 0.1%
26.941
< 0.1%
26.832
< 0.1%

sec_time5
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct285
Distinct (%)34.7%
Missing5528
Missing (%)87.1%
Infinite0
Infinite (%)0.0%
Mean23.86868453
Minimum21.81
Maximum26.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.2 KiB
2022-01-09T21:24:40.638356image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum21.81
5-th percentile22.68
Q123.41
median23.83
Q324.28
95-th percentile25.26
Maximum26.5
Range4.69
Interquartile range (IQR)0.87

Descriptive statistics

Standard deviation0.7588595256
Coefficient of variation (CV)0.03179310216
Kurtosis0.6041584821
Mean23.86868453
Median Absolute Deviation (MAD)0.44
Skewness0.3754405135
Sum19596.19
Variance0.5758677796
MonotonicityNot monotonic
2022-01-09T21:24:40.768373image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.7610
 
0.2%
24.1410
 
0.2%
23.269
 
0.1%
23.989
 
0.1%
23.89
 
0.1%
24.169
 
0.1%
23.848
 
0.1%
23.668
 
0.1%
23.778
 
0.1%
24.188
 
0.1%
Other values (275)733
 
11.5%
(Missing)5528
87.1%
ValueCountFrequency (%)
21.811
< 0.1%
21.891
< 0.1%
21.932
< 0.1%
22.021
< 0.1%
22.091
< 0.1%
22.11
< 0.1%
22.111
< 0.1%
22.151
< 0.1%
22.21
< 0.1%
22.231
< 0.1%
ValueCountFrequency (%)
26.51
< 0.1%
26.421
< 0.1%
26.121
< 0.1%
26.081
< 0.1%
26.071
< 0.1%
26.051
< 0.1%
26.041
< 0.1%
26.021
< 0.1%
25.952
< 0.1%
25.911
< 0.1%

sec_time6
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct98
Distinct (%)85.2%
Missing6234
Missing (%)98.2%
Infinite0
Infinite (%)0.0%
Mean23.91226087
Minimum21.77
Maximum25.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.2 KiB
2022-01-09T21:24:40.885408image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum21.77
5-th percentile22.824
Q123.55
median24.02
Q324.295
95-th percentile24.873
Maximum25.92
Range4.15
Interquartile range (IQR)0.745

Descriptive statistics

Standard deviation0.667664246
Coefficient of variation (CV)0.02792141862
Kurtosis0.9512279413
Mean23.91226087
Median Absolute Deviation (MAD)0.38
Skewness-0.2524265974
Sum2749.91
Variance0.4457755454
MonotonicityNot monotonic
2022-01-09T21:24:40.991430image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.183
 
< 0.1%
24.053
 
< 0.1%
23.912
 
< 0.1%
24.32
 
< 0.1%
23.792
 
< 0.1%
23.782
 
< 0.1%
24.072
 
< 0.1%
24.042
 
< 0.1%
24.222
 
< 0.1%
24.472
 
< 0.1%
Other values (88)93
 
1.5%
(Missing)6234
98.2%
ValueCountFrequency (%)
21.771
< 0.1%
22.151
< 0.1%
22.511
< 0.1%
22.621
< 0.1%
22.641
< 0.1%
22.741
< 0.1%
22.861
< 0.1%
22.871
< 0.1%
22.961
< 0.1%
22.971
< 0.1%
ValueCountFrequency (%)
25.921
< 0.1%
25.531
< 0.1%
25.281
< 0.1%
25.151
< 0.1%
24.921
< 0.1%
24.881
< 0.1%
24.871
< 0.1%
24.861
< 0.1%
24.821
< 0.1%
24.721
< 0.1%

sec_time7
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing6349
Missing (%)100.0%
Memory size99.2 KiB

time1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct660
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.69946606
Minimum12.39
Maximum30.03
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.2 KiB
2022-01-09T21:24:41.099461image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum12.39
5-th percentile12.99
Q113.69
median23.72
Q324.7
95-th percentile28.16
Maximum30.03
Range17.64
Interquartile range (IQR)11.01

Descriptive statistics

Standard deviation5.880318926
Coefficient of variation (CV)0.2840807058
Kurtosis-1.649093239
Mean20.69946606
Median Absolute Deviation (MAD)4.05
Skewness-0.2802923861
Sum131420.91
Variance34.57815068
MonotonicityNot monotonic
2022-01-09T21:24:41.200487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.5958
 
0.9%
13.6258
 
0.9%
13.5656
 
0.9%
13.6754
 
0.9%
13.6453
 
0.8%
13.752
 
0.8%
13.5351
 
0.8%
13.5248
 
0.8%
13.5545
 
0.7%
13.6944
 
0.7%
Other values (650)5830
91.8%
ValueCountFrequency (%)
12.392
< 0.1%
12.412
< 0.1%
12.472
< 0.1%
12.482
< 0.1%
12.491
 
< 0.1%
12.511
 
< 0.1%
12.522
< 0.1%
12.534
0.1%
12.541
 
< 0.1%
12.554
0.1%
ValueCountFrequency (%)
30.031
< 0.1%
29.641
< 0.1%
29.51
< 0.1%
29.471
< 0.1%
29.341
< 0.1%
29.251
< 0.1%
29.241
< 0.1%
29.231
< 0.1%
29.221
< 0.1%
29.192
< 0.1%

time2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1308
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.52621515
Minimum33.11
Maximum56.22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.2 KiB
2022-01-09T21:24:41.503540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum33.11
5-th percentile34.18
Q135.95
median46.41
Q348.07
95-th percentile52.52
Maximum56.22
Range23.11
Interquartile range (IQR)12.12

Descriptive statistics

Standard deviation6.657224782
Coefficient of variation (CV)0.1529474768
Kurtosis-1.536184241
Mean43.52621515
Median Absolute Deviation (MAD)5.25
Skewness-0.2239861298
Sum276347.94
Variance44.3186418
MonotonicityNot monotonic
2022-01-09T21:24:41.613573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46.826
 
0.4%
47.0324
 
0.4%
46.5323
 
0.4%
46.7521
 
0.3%
47.1120
 
0.3%
46.3920
 
0.3%
46.8620
 
0.3%
47.3920
 
0.3%
47.0519
 
0.3%
4719
 
0.3%
Other values (1298)6137
96.7%
ValueCountFrequency (%)
33.111
 
< 0.1%
33.221
 
< 0.1%
33.291
 
< 0.1%
33.32
< 0.1%
33.321
 
< 0.1%
33.331
 
< 0.1%
33.341
 
< 0.1%
33.361
 
< 0.1%
33.381
 
< 0.1%
33.394
0.1%
ValueCountFrequency (%)
56.221
< 0.1%
55.521
< 0.1%
55.371
< 0.1%
55.171
< 0.1%
55.111
< 0.1%
54.752
< 0.1%
54.711
< 0.1%
54.611
< 0.1%
54.591
< 0.1%
54.561
< 0.1%

time3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1715
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.35695858
Minimum55.16
Maximum81.29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.2 KiB
2022-01-09T21:24:41.730603image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum55.16
5-th percentile57.12
Q159.83
median69.72
Q372.1
95-th percentile77.45
Maximum81.29
Range26.13
Interquartile range (IQR)12.27

Descriptive statistics

Standard deviation6.978638154
Coefficient of variation (CV)0.1036067884
Kurtosis-1.364604657
Mean67.35695858
Median Absolute Deviation (MAD)6.63
Skewness-0.1460940021
Sum427649.33
Variance48.70139048
MonotonicityNot monotonic
2022-01-09T21:24:41.844623image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70.117
 
0.3%
69.9917
 
0.3%
69.7317
 
0.3%
69.8916
 
0.3%
70.6716
 
0.3%
69.7416
 
0.3%
70.6515
 
0.2%
70.1815
 
0.2%
70.3915
 
0.2%
69.5415
 
0.2%
Other values (1705)6190
97.5%
ValueCountFrequency (%)
55.161
 
< 0.1%
55.371
 
< 0.1%
55.381
 
< 0.1%
55.421
 
< 0.1%
55.493
< 0.1%
55.541
 
< 0.1%
55.551
 
< 0.1%
55.563
< 0.1%
55.571
 
< 0.1%
55.591
 
< 0.1%
ValueCountFrequency (%)
81.291
< 0.1%
80.841
< 0.1%
80.421
< 0.1%
80.151
< 0.1%
80.081
< 0.1%
79.911
< 0.1%
79.821
< 0.1%
79.811
< 0.1%
79.671
< 0.1%
79.641
< 0.1%

time4
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1403
Distinct (%)38.6%
Missing2715
Missing (%)42.8%
Infinite0
Infinite (%)0.0%
Mean91.73579252
Minimum80.43
Maximum107.81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.2 KiB
2022-01-09T21:24:41.953640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum80.43
5-th percentile81.94
Q183.41
median94.3
Q3100.03
95-th percentile101.73
Maximum107.81
Range27.38
Interquartile range (IQR)16.62

Descriptive statistics

Standard deviation7.814996903
Coefficient of variation (CV)0.08519026967
Kurtosis-1.720723382
Mean91.73579252
Median Absolute Deviation (MAD)7.27
Skewness0.006925667848
Sum333367.87
Variance61.07417659
MonotonicityNot monotonic
2022-01-09T21:24:42.325738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82.9713
 
0.2%
82.2513
 
0.2%
83.1311
 
0.2%
82.8911
 
0.2%
82.410
 
0.2%
82.8710
 
0.2%
100.710
 
0.2%
100.8810
 
0.2%
82.5710
 
0.2%
82.9410
 
0.2%
Other values (1393)3526
55.5%
(Missing)2715
42.8%
ValueCountFrequency (%)
80.431
< 0.1%
80.521
< 0.1%
80.631
< 0.1%
80.711
< 0.1%
80.721
< 0.1%
80.732
< 0.1%
80.762
< 0.1%
80.841
< 0.1%
80.851
< 0.1%
80.881
< 0.1%
ValueCountFrequency (%)
107.811
< 0.1%
106.641
< 0.1%
105.321
< 0.1%
104.731
< 0.1%
104.51
< 0.1%
104.441
< 0.1%
104.111
< 0.1%
104.051
< 0.1%
104.031
< 0.1%
103.971
< 0.1%

time5
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct551
Distinct (%)67.1%
Missing5528
Missing (%)87.1%
Infinite0
Infinite (%)0.0%
Mean112.4796955
Minimum105.83
Maximum134.31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.2 KiB
2022-01-09T21:24:42.432748image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum105.83
5-th percentile107.67
Q1109.01
median110.33
Q3113.15
95-th percentile124.29
Maximum134.31
Range28.48
Interquartile range (IQR)4.14

Descriptive statistics

Standard deviation5.452576413
Coefficient of variation (CV)0.04847609508
Kurtosis0.8089396169
Mean112.4796955
Median Absolute Deviation (MAD)1.65
Skewness1.446455725
Sum92345.83
Variance29.73058954
MonotonicityNot monotonic
2022-01-09T21:24:42.530783image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
109.736
 
0.1%
110.075
 
0.1%
110.945
 
0.1%
109.345
 
0.1%
108.85
 
0.1%
108.675
 
0.1%
109.985
 
0.1%
109.454
 
0.1%
109.474
 
0.1%
109.094
 
0.1%
Other values (541)773
 
12.2%
(Missing)5528
87.1%
ValueCountFrequency (%)
105.831
< 0.1%
105.881
< 0.1%
106.461
< 0.1%
106.481
< 0.1%
106.621
< 0.1%
106.821
< 0.1%
106.881
< 0.1%
107.011
< 0.1%
107.051
< 0.1%
107.061
< 0.1%
ValueCountFrequency (%)
134.311
< 0.1%
130.881
< 0.1%
128.871
< 0.1%
127.771
< 0.1%
127.471
< 0.1%
127.391
< 0.1%
126.971
< 0.1%
126.551
< 0.1%
126.141
< 0.1%
126.091
< 0.1%

time6
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct111
Distinct (%)96.5%
Missing6234
Missing (%)98.2%
Infinite0
Infinite (%)0.0%
Mean140.3497391
Minimum132.84
Maximum158.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.2 KiB
2022-01-09T21:24:42.634807image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum132.84
5-th percentile135.947
Q1137.29
median138.36
Q3140.205
95-th percentile149.875
Maximum158.49
Range25.65
Interquartile range (IQR)2.915

Descriptive statistics

Standard deviation4.910272344
Coefficient of variation (CV)0.03498597414
Kurtosis1.109661206
Mean140.3497391
Median Absolute Deviation (MAD)1.32
Skewness1.375067173
Sum16140.22
Variance24.11077449
MonotonicityNot monotonic
2022-01-09T21:24:42.755153image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
139.162
 
< 0.1%
139.92
 
< 0.1%
138.32
 
< 0.1%
138.622
 
< 0.1%
137.171
 
< 0.1%
149.831
 
< 0.1%
137.241
 
< 0.1%
140.191
 
< 0.1%
148.111
 
< 0.1%
137.551
 
< 0.1%
Other values (101)101
 
1.6%
(Missing)6234
98.2%
ValueCountFrequency (%)
132.841
< 0.1%
133.691
< 0.1%
135.391
< 0.1%
135.441
< 0.1%
135.661
< 0.1%
135.871
< 0.1%
135.981
< 0.1%
1361
< 0.1%
136.041
< 0.1%
136.061
< 0.1%
ValueCountFrequency (%)
158.491
< 0.1%
153.621
< 0.1%
150.841
< 0.1%
150.71
< 0.1%
150.641
< 0.1%
149.981
< 0.1%
149.831
< 0.1%
149.751
< 0.1%
149.141
< 0.1%
148.731
< 0.1%

time7
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing6349
Missing (%)100.0%
Memory size99.2 KiB

place_combination1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct14
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.238620255
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.2 KiB
2022-01-09T21:24:42.844167image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum14
Range13
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.618580192
Coefficient of variation (CV)0.5800289237
Kurtosis-1.026767281
Mean6.238620255
Median Absolute Deviation (MAD)3
Skewness0.2343809874
Sum39609
Variance13.09412261
MonotonicityNot monotonic
2022-01-09T21:24:42.924178image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1675
10.6%
4591
9.3%
2571
9.0%
3547
8.6%
5543
8.6%
7528
8.3%
6515
8.1%
10488
7.7%
8482
7.6%
9445
7.0%
Other values (4)964
15.2%
ValueCountFrequency (%)
1675
10.6%
2571
9.0%
3547
8.6%
4591
9.3%
5543
8.6%
6515
8.1%
7528
8.3%
8482
7.6%
9445
7.0%
10488
7.7%
ValueCountFrequency (%)
14111
 
1.7%
13128
 
2.0%
12352
5.5%
11373
5.9%
10488
7.7%
9445
7.0%
8482
7.6%
7528
8.3%
6515
8.1%
5543
8.6%

place_combination2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct14
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.366356907
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.2 KiB
2022-01-09T21:24:42.999211image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum14
Range13
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.662594129
Coefficient of variation (CV)0.5753045553
Kurtosis-1.024609946
Mean6.366356907
Median Absolute Deviation (MAD)3
Skewness0.2350581497
Sum40420
Variance13.41459575
MonotonicityNot monotonic
2022-01-09T21:24:43.080213image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1605
9.5%
2595
9.4%
4586
9.2%
3544
8.6%
5530
8.3%
6530
8.3%
9494
7.8%
7488
7.7%
8485
7.6%
10444
7.0%
Other values (4)1048
16.5%
ValueCountFrequency (%)
1605
9.5%
2595
9.4%
3544
8.6%
4586
9.2%
5530
8.3%
6530
8.3%
7488
7.7%
8485
7.6%
9494
7.8%
10444
7.0%
ValueCountFrequency (%)
14146
 
2.3%
13170
 
2.7%
12331
5.2%
11401
6.3%
10444
7.0%
9494
7.8%
8485
7.6%
7488
7.7%
6530
8.3%
5530
8.3%

place_combination3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct14
Distinct (%)0.2%
Missing25
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean6.525932954
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.2 KiB
2022-01-09T21:24:43.166248image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q310
95-th percentile13
Maximum14
Range13
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.73168461
Coefficient of variation (CV)0.5718239272
Kurtosis-1.095898637
Mean6.525932954
Median Absolute Deviation (MAD)3
Skewness0.1763539708
Sum41270
Variance13.92547003
MonotonicityNot monotonic
2022-01-09T21:24:43.248266image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1602
9.5%
2585
9.2%
5568
8.9%
4522
8.2%
9517
8.1%
3509
8.0%
10477
7.5%
7473
7.4%
6466
7.3%
8460
7.2%
Other values (4)1145
18.0%
ValueCountFrequency (%)
1602
9.5%
2585
9.2%
3509
8.0%
4522
8.2%
5568
8.9%
6466
7.3%
7473
7.4%
8460
7.2%
9517
8.1%
10477
7.5%
ValueCountFrequency (%)
14152
 
2.4%
13200
 
3.2%
12382
6.0%
11411
6.5%
10477
7.5%
9517
8.1%
8460
7.2%
7473
7.4%
6466
7.3%
5568
8.9%

place_combination4
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct10
Distinct (%)43.5%
Missing6326
Missing (%)99.6%
Infinite0
Infinite (%)0.0%
Mean8.434782609
Minimum4
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.2 KiB
2022-01-09T21:24:43.323282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q16.5
median8
Q311
95-th percentile12
Maximum14
Range10
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation2.982160397
Coefficient of variation (CV)0.3535550986
Kurtosis-1.04685992
Mean8.434782609
Median Absolute Deviation (MAD)3
Skewness0.05426980722
Sum194
Variance8.893280632
MonotonicityNot monotonic
2022-01-09T21:24:43.400299image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
84
 
0.1%
124
 
0.1%
73
 
< 0.1%
43
 
< 0.1%
102
 
< 0.1%
52
 
< 0.1%
112
 
< 0.1%
61
 
< 0.1%
141
 
< 0.1%
91
 
< 0.1%
(Missing)6326
99.6%
ValueCountFrequency (%)
43
< 0.1%
52
< 0.1%
61
 
< 0.1%
73
< 0.1%
84
0.1%
91
 
< 0.1%
102
< 0.1%
112
< 0.1%
124
0.1%
141
 
< 0.1%
ValueCountFrequency (%)
141
 
< 0.1%
124
0.1%
112
< 0.1%
102
< 0.1%
91
 
< 0.1%
84
0.1%
73
< 0.1%
61
 
< 0.1%
52
< 0.1%
43
< 0.1%

place_dividend1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct279
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.77846905
Minimum10.1
Maximum410.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.2 KiB
2022-01-09T21:24:43.495307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10.1
5-th percentile11.5
Q115
median20
Q331
95-th percentile69
Maximum410.5
Range400.4
Interquartile range (IQR)16

Descriptive statistics

Standard deviation25.17592501
Coefficient of variation (CV)0.9063107461
Kurtosis44.45493577
Mean27.77846905
Median Absolute Deviation (MAD)6
Skewness5.102480959
Sum176365.5
Variance633.8272002
MonotonicityNot monotonic
2022-01-09T21:24:43.761370image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.5218
 
3.4%
13.5212
 
3.3%
14212
 
3.3%
16203
 
3.2%
16.5195
 
3.1%
15190
 
3.0%
15.5182
 
2.9%
12.5181
 
2.9%
12180
 
2.8%
13177
 
2.8%
Other values (269)4399
69.3%
ValueCountFrequency (%)
10.190
1.4%
10.552
 
0.8%
1181
 
1.3%
11.5116
1.8%
12180
2.8%
12.5181
2.9%
13177
2.8%
13.5212
3.3%
14212
3.3%
14.5218
3.4%
ValueCountFrequency (%)
410.51
< 0.1%
4081
< 0.1%
3621
< 0.1%
3441
< 0.1%
331.51
< 0.1%
3191
< 0.1%
2671
< 0.1%
257.51
< 0.1%
243.51
< 0.1%
2311
< 0.1%

place_dividend2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct315
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.80253583
Minimum10.1
Maximum627
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.2 KiB
2022-01-09T21:24:43.883394image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10.1
5-th percentile12.5
Q117
median24
Q336.5
95-th percentile83.5
Maximum627
Range616.9
Interquartile range (IQR)19.5

Descriptive statistics

Standard deviation30.27542251
Coefficient of variation (CV)0.9229598182
Kurtosis47.23818703
Mean32.80253583
Median Absolute Deviation (MAD)8.5
Skewness5.008817106
Sum208263.3
Variance916.6012081
MonotonicityNot monotonic
2022-01-09T21:24:43.997420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.5173
 
2.7%
16.5170
 
2.7%
15168
 
2.6%
17.5159
 
2.5%
18156
 
2.5%
14.5150
 
2.4%
16149
 
2.3%
14138
 
2.2%
17136
 
2.1%
13.5132
 
2.1%
Other values (305)4818
75.9%
ValueCountFrequency (%)
10.133
 
0.5%
10.531
 
0.5%
1155
 
0.9%
11.561
1.0%
1283
1.3%
12.594
1.5%
13117
1.8%
13.5132
2.1%
14138
2.2%
14.5150
2.4%
ValueCountFrequency (%)
6271
< 0.1%
4081
< 0.1%
370.51
< 0.1%
348.51
< 0.1%
3311
< 0.1%
328.51
< 0.1%
322.51
< 0.1%
3001
< 0.1%
2901
< 0.1%
2821
< 0.1%

place_dividend3
Real number (ℝ≥0)

HIGH CORRELATION

Distinct368
Distinct (%)5.8%
Missing25
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean38.96929159
Minimum10.1
Maximum420.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.2 KiB
2022-01-09T21:24:44.108687image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10.1
5-th percentile13.5
Q118.5
median27.5
Q344
95-th percentile103.5
Maximum420.5
Range410.4
Interquartile range (IQR)25.5

Descriptive statistics

Standard deviation37.01593819
Coefficient of variation (CV)0.9498745469
Kurtosis22.50243744
Mean38.96929159
Median Absolute Deviation (MAD)10.5
Skewness3.914955077
Sum246441.8
Variance1370.17968
MonotonicityNot monotonic
2022-01-09T21:24:44.222057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.5138
 
2.2%
16.5129
 
2.0%
17129
 
2.0%
18128
 
2.0%
16123
 
1.9%
15122
 
1.9%
19.5119
 
1.9%
19117
 
1.8%
20116
 
1.8%
14.5114
 
1.8%
Other values (358)5089
80.2%
ValueCountFrequency (%)
10.123
 
0.4%
10.520
 
0.3%
1120
 
0.3%
11.551
0.8%
1243
 
0.7%
12.551
0.8%
1395
1.5%
13.597
1.5%
14112
1.8%
14.5114
1.8%
ValueCountFrequency (%)
420.51
< 0.1%
402.51
< 0.1%
3951
< 0.1%
393.51
< 0.1%
390.51
< 0.1%
375.51
< 0.1%
373.51
< 0.1%
343.51
< 0.1%
3341
< 0.1%
3311
< 0.1%

place_dividend4
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct15
Distinct (%)65.2%
Missing6326
Missing (%)99.6%
Infinite0
Infinite (%)0.0%
Mean22.01304348
Minimum10.1
Maximum68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.2 KiB
2022-01-09T21:24:44.328191image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10.1
5-th percentile10.1
Q110.1
median15.5
Q324.75
95-th percentile58.05
Maximum68
Range57.9
Interquartile range (IQR)14.65

Descriptive statistics

Standard deviation16.75780156
Coefficient of variation (CV)0.7612669086
Kurtosis1.989246606
Mean22.01304348
Median Absolute Deviation (MAD)5.4
Skewness1.673798784
Sum506.3
Variance280.823913
MonotonicityNot monotonic
2022-01-09T21:24:44.402488image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
10.18
 
0.1%
36.52
 
< 0.1%
16.51
 
< 0.1%
161
 
< 0.1%
591
 
< 0.1%
24.51
 
< 0.1%
12.51
 
< 0.1%
18.51
 
< 0.1%
15.51
 
< 0.1%
681
 
< 0.1%
Other values (5)5
 
0.1%
(Missing)6326
99.6%
ValueCountFrequency (%)
10.18
0.1%
121
 
< 0.1%
12.51
 
< 0.1%
14.51
 
< 0.1%
15.51
 
< 0.1%
161
 
< 0.1%
16.51
 
< 0.1%
18.51
 
< 0.1%
211
 
< 0.1%
24.51
 
< 0.1%
ValueCountFrequency (%)
681
< 0.1%
591
< 0.1%
49.51
< 0.1%
36.52
< 0.1%
251
< 0.1%
24.51
< 0.1%
211
< 0.1%
18.51
< 0.1%
16.51
< 0.1%
161
< 0.1%

win_combination1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct14
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.238620255
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.2 KiB
2022-01-09T21:24:44.482119image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum14
Range13
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.620321119
Coefficient of variation (CV)0.5803079801
Kurtosis-1.028534225
Mean6.238620255
Median Absolute Deviation (MAD)3
Skewness0.2340520952
Sum39609
Variance13.106725
MonotonicityNot monotonic
2022-01-09T21:24:44.585803image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1676
10.6%
4590
9.3%
2572
9.0%
3547
8.6%
5543
8.6%
7526
8.3%
6514
8.1%
10488
7.7%
8482
7.6%
9446
7.0%
Other values (4)965
15.2%
ValueCountFrequency (%)
1676
10.6%
2572
9.0%
3547
8.6%
4590
9.3%
5543
8.6%
6514
8.1%
7526
8.3%
8482
7.6%
9446
7.0%
10488
7.7%
ValueCountFrequency (%)
14111
 
1.7%
13128
 
2.0%
12353
5.6%
11373
5.9%
10488
7.7%
9446
7.0%
8482
7.6%
7526
8.3%
6514
8.1%
5543
8.6%

win_dividend1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct776
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.0969444
Minimum10.5
Maximum2687.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.2 KiB
2022-01-09T21:24:44.705613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10.5
5-th percentile19.5
Q134.5
median58.5
Q3105.5
95-th percentile283.3
Maximum2687.5
Range2677
Interquartile range (IQR)71

Descriptive statistics

Standard deviation131.2212594
Coefficient of variation (CV)1.365509176
Kurtosis73.85875053
Mean96.0969444
Median Absolute Deviation (MAD)29
Skewness6.531577685
Sum610119.5
Variance17219.01892
MonotonicityNot monotonic
2022-01-09T21:24:44.818640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.558
 
0.9%
2351
 
0.8%
34.550
 
0.8%
30.549
 
0.8%
2648
 
0.8%
2048
 
0.8%
3148
 
0.8%
26.548
 
0.8%
41.548
 
0.8%
35.547
 
0.7%
Other values (766)5854
92.2%
ValueCountFrequency (%)
10.52
 
< 0.1%
111
 
< 0.1%
11.52
 
< 0.1%
122
 
< 0.1%
12.54
 
0.1%
135
 
0.1%
13.57
 
0.1%
1411
0.2%
14.515
0.2%
1519
0.3%
ValueCountFrequency (%)
2687.51
< 0.1%
2454.51
< 0.1%
1835.51
< 0.1%
17441
< 0.1%
1606.51
< 0.1%
1603.51
< 0.1%
15791
< 0.1%
1561.51
< 0.1%
1362.51
< 0.1%
12761
< 0.1%

win_combination2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct7
Distinct (%)58.3%
Missing6337
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean8.166666667
Minimum3
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.2 KiB
2022-01-09T21:24:44.910166image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3.55
Q15.5
median8
Q311.25
95-th percentile12
Maximum12
Range9
Interquartile range (IQR)5.75

Descriptive statistics

Standard deviation3.325748947
Coefficient of variation (CV)0.407234565
Kurtosis-1.378705198
Mean8.166666667
Median Absolute Deviation (MAD)3.5
Skewness-0.281407414
Sum98
Variance11.06060606
MonotonicityNot monotonic
2022-01-09T21:24:44.982182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
123
 
< 0.1%
83
 
< 0.1%
42
 
< 0.1%
111
 
< 0.1%
61
 
< 0.1%
31
 
< 0.1%
101
 
< 0.1%
(Missing)6337
99.8%
ValueCountFrequency (%)
31
 
< 0.1%
42
< 0.1%
61
 
< 0.1%
83
< 0.1%
101
 
< 0.1%
111
 
< 0.1%
123
< 0.1%
ValueCountFrequency (%)
123
< 0.1%
111
 
< 0.1%
101
 
< 0.1%
83
< 0.1%
61
 
< 0.1%
42
< 0.1%
31
 
< 0.1%

win_dividend2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct12
Distinct (%)100.0%
Missing6337
Missing (%)99.8%
Infinite0
Infinite (%)0.0%
Mean101.4166667
Minimum12
Maximum282.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size99.2 KiB
2022-01-09T21:24:45.059201image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile16.675
Q125.25
median50.25
Q3178.625
95-th percentile264.075
Maximum282.5
Range270.5
Interquartile range (IQR)153.375

Descriptive statistics

Standard deviation101.6725657
Coefficient of variation (CV)1.002523244
Kurtosis-0.9184888934
Mean101.4166667
Median Absolute Deviation (MAD)28.5
Skewness0.9263869947
Sum1217
Variance10337.31061
MonotonicityNot monotonic
2022-01-09T21:24:45.430282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2361
 
< 0.1%
35.51
 
< 0.1%
77.51
 
< 0.1%
30.51
 
< 0.1%
282.51
 
< 0.1%
261
 
< 0.1%
121
 
< 0.1%
20.51
 
< 0.1%
651
 
< 0.1%
231
 
< 0.1%
Other values (2)2
 
< 0.1%
(Missing)6337
99.8%
ValueCountFrequency (%)
121
< 0.1%
20.51
< 0.1%
231
< 0.1%
261
< 0.1%
30.51
< 0.1%
35.51
< 0.1%
651
< 0.1%
77.51
< 0.1%
159.51
< 0.1%
2361
< 0.1%
ValueCountFrequency (%)
282.51
< 0.1%
2491
< 0.1%
2361
< 0.1%
159.51
< 0.1%
77.51
< 0.1%
651
< 0.1%
35.51
< 0.1%
30.51
< 0.1%
261
< 0.1%
231
< 0.1%

Interactions

2022-01-09T21:24:33.144198image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:19.358886image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:21.814150image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:25.369612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:28.025485image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:30.778531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:33.279374image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:35.949531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:38.560732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:40.954141image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:43.765484image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:46.175035image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:48.983763image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:51.338284image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:53.937871image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:56.545323image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:59.093902image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:01.508355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:04.444943image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:07.444154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:09.994727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:12.727347image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:15.897049image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:19.275056image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:21.764246image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:24.225802image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:27.249467image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:30.357566image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:33.224228image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:19.468071image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:21.903171image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:25.452633image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:28.108509image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:30.864571image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:33.364382image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:36.033551image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:38.644769image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:41.038178image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:43.851500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:46.276055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:49.065782image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:51.422300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:54.020883image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:56.634347image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:59.167920image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:01.621383image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:04.555162image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:07.531170image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:10.074753image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:12.839367image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:16.029078image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:19.364062image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:21.845249image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:24.311819image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:27.359024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:30.442600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:33.305230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:19.562057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:21.999792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:25.543651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:28.203519image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:30.961594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:33.457418image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:36.126570image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:38.731799image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:41.395624image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:43.931532image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:46.379086image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:49.154802image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:51.512332image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:54.105909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:56.733369image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:59.247932image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:01.730416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:04.669187image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:07.627196image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:10.154756image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:12.941403image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:16.177112image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:19.477088image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:21.930268image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:24.406826image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:27.463054image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:30.521619image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:33.380261image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:19.651076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:22.093222image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:25.629676image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:28.296540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:31.059619image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:33.549425image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:36.214576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:38.819838image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:41.486653image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:44.013550image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:46.487106image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:49.241821image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:51.598346image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:54.195935image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:56.825385image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:59.551579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:01.823439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:04.772959image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:07.719216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:10.233784image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:13.040406image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:16.317144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:19.575118image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:22.020289image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:24.514850image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:27.555014image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:30.601622image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:33.578532image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:19.746124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:22.184242image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:25.719697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:28.383573image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:31.153641image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-01-09T21:24:04.184000image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:07.205105image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:09.754665image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:12.493296image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:15.605992image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:18.741396image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:21.533179image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:24.006752image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:26.924393image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:30.094509image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:32.912152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:35.712377image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:21.667117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:25.227369image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:27.887462image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:30.420960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:33.131330image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:35.805484image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:38.407650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:40.821091image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:43.597456image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:46.015001image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:48.840635image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:51.190262image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:53.807850image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:56.421301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:58.943868image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:01.328297image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:04.276023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:07.293127image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:09.842702image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:12.572310image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:15.703005image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:18.832419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:21.618199image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:24.082755image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:27.056424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:30.190529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:32.985174image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:35.784390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:21.740134image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:25.297385image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:27.954478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:30.486975image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:33.204348image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:35.875500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:38.481905image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:40.887110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:43.684471image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:46.101013image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:48.909749image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:51.262278image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:53.871863image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:56.483314image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:23:59.025887image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:01.417333image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:04.352051image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:07.365127image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:09.917702image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:12.649328image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:15.783022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:19.195025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:21.690223image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:24.151772image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:27.158454image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:30.271547image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-01-09T21:24:33.054178image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-01-09T21:24:45.544307image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-01-09T21:24:45.822370image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-01-09T21:24:46.113440image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-01-09T21:24:46.370494image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-01-09T21:24:46.536632image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-01-09T21:24:36.134458image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-01-09T21:24:36.873148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-01-09T21:24:37.176171image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-01-09T21:24:37.372208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

datevenuerace_noconfigsurfacedistancegoinghorse_ratingsprizerace_classsec_time1sec_time2sec_time3sec_time4sec_time5sec_time6sec_time7time1time2time3time4time5time6time7place_combination1place_combination2place_combination3place_combination4place_dividend1place_dividend2place_dividend3place_dividend4win_combination1win_dividend1win_combination2win_dividend2
race_id
01997-06-02ST1A01400GOOD TO FIRM40-15485000.0513.5321.5923.9423.58NaNNaNNaN13.5335.1259.0682.64NaNNaNNaN8116.0NaN36.525.518.0NaN8121.0NaNNaN
11997-06-02ST2A01200GOOD TO FIRM40-15485000.0524.0522.6423.70NaNNaNNaNNaN24.0546.6970.39NaNNaNNaNNaN5134.0NaN12.547.033.5NaN523.5NaNNaN
21997-06-02ST3A01400GOOD TO FIRM60-40625000.0413.7722.2224.8822.82NaNNaNNaN13.7735.9960.8783.69NaNNaNNaN11113.0NaN23.023.059.5NaN1170.0NaNNaN
31997-06-02ST4A01200GOOD TO FIRM120-951750000.0124.3322.4722.09NaNNaNNaNNaN24.3346.8068.89NaNNaNNaNNaN5310.0NaN14.024.516.0NaN552.0NaNNaN
41997-06-02ST5A01600GOOD TO FIRM60-40625000.0425.4523.5223.3123.56NaNNaNNaN25.4548.9772.2895.84NaNNaNNaN2101.0NaN15.528.017.5NaN236.5NaNNaN
51997-06-02ST6A01200GOOD TO FIRM60-40625000.0423.4722.4823.25NaNNaNNaNNaN23.4745.9569.20NaNNaNNaNNaN9148.0NaN16.5408.070.0NaN961.0NaNNaN
61997-06-02ST7A01400GOOD TO FIRM100-801150000.0213.7521.6122.8123.34NaNNaNNaN13.7535.3658.1781.51NaNNaNNaN576.0NaN41.521.579.0NaN5160.5NaNNaN
71997-06-02ST8A01000GOOD TO FIRM100-801150000.0213.5520.5622.44NaNNaNNaNNaN13.5534.1156.55NaNNaNNaNNaN1338.0NaN23.544.518.0NaN1370.5NaNNaN
81997-06-02ST9A01400GOOD TO FIRM80-60850000.0313.3122.2023.9223.16NaNNaNNaN13.3135.5159.4382.59NaNNaNNaN752.0NaN12.516.533.5NaN724.0NaNNaN
91997-06-02ST10A01200GOOD TO FIRM80-60850000.0324.0823.4522.66NaNNaNNaNNaN24.0847.5370.19NaNNaNNaNNaN1136.0NaN49.545.514.0NaN1203.0NaNNaN

Last rows

datevenuerace_noconfigsurfacedistancegoinghorse_ratingsprizerace_classsec_time1sec_time2sec_time3sec_time4sec_time5sec_time6sec_time7time1time2time3time4time5time6time7place_combination1place_combination2place_combination3place_combination4place_dividend1place_dividend2place_dividend3place_dividend4win_combination1win_dividend1win_combination2win_dividend2
race_id
63392005-08-28ST1A01200GOOD80-601235000.0323.3722.3923.65NaNNaNNaNNaN23.3745.7669.41NaNNaNNaNNaN2144.0NaN31.529.510.1NaN2216.0NaNNaN
63402005-08-28ST2A01800GOOD80-551235000.0314.1522.9824.0923.9823.18NaNNaN14.1537.1361.2285.20108.38NaNNaN418.0NaN19.549.070.0NaN455.0NaNNaN
63412005-08-28ST3A01400GOOD80-601235000.0313.4522.3923.1823.14NaNNaNNaN13.4535.8459.0282.16NaNNaNNaN3115.0NaN16.510.523.5NaN349.5NaNNaN
63422005-08-28ST4A02400GOODG16500000.01125.2924.4824.5524.3523.4224.13NaN25.2949.7774.3298.67122.09146.22NaN417.0NaN41.510.539.5NaN4204.5NaNNaN
63432005-08-28ST5A01200GOODG18500000.01123.9022.1222.78NaNNaNNaNNaN23.9046.0268.80NaNNaNNaNNaN123.0NaN19.012.033.0NaN175.5NaNNaN
63442005-08-28ST6A01400GOOD115-952500000.0113.6523.1024.6021.78NaNNaNNaN13.6536.7561.3583.13NaNNaNNaN675.0NaN13.514.562.5NaN638.0NaNNaN
63452005-08-28ST7A01600GOODG23000000.01124.4822.5423.0123.45NaNNaNNaN24.4847.0270.0393.48NaNNaNNaN5614.0NaN18.020.545.0NaN560.0NaNNaN
63462005-08-28ST8A02000GOODG25000000.01126.7923.3123.2423.4624.15NaNNaN26.7950.1073.3496.80120.95NaNNaN285.0NaN11.027.538.5NaN217.5NaNNaN
63472005-08-28ST9A01200GOOD100-801750000.0223.6522.5422.97NaNNaNNaNNaN23.6546.1969.16NaNNaNNaNNaN964.0NaN11.037.021.5NaN916.0NaNNaN
63482005-08-28ST10A01600GOOD100-801750000.0225.4624.0623.7322.71NaNNaNNaN25.4649.5273.2595.96NaNNaNNaN248.0NaN38.063.018.5NaN2134.0NaNNaN